Development of a control model for predictive control data to extend the process limits of piezokinematic systems for production technology.

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The aim of the project is to develop a predictive control model for highly dynamic piezo axes for use in machine tools. With the aid of the control model to be developed, it should be possible to use the additional axes above the permissible controller bandwith. The control model should be used to predict the position accuratly without having to go through a complex iterative process. This should help to extend the production limits of existing machine tool systems and to increase the flexibility and performance of piezo systems. The modeling shall be based on tactile measured geometry data of manufactured workpieces. By using the workpiece measurement data, dynamic influences from the tool and the workpiece are to be included into the model. For modeling from the workpiece measurement data, suitable methods of system identification must be compared and evaluated with each other. In view of current technological developments, one focus should be put on methods of machine learning with neural networks. For modeling purposes, the measured workpiece data must be transformed into the time, frequency or laplace domain to extract suitable characteristics. The bandwidth of the model is determined by the selected test geometries used for the workpiece machining. In order to increase the model bandwidth, the type of test geometries, the machining speed, the angular alignment of the geometry in the workpiece and the deflection mode of the cutting edge must be varied. A 3-axis machining centre with additional piezo axes is available for teh and verification under process conditions. The necessary reproducibility or time invariance of the machine behaviour is guaranteed.